Methods to incorporate machine learning analytics for optimizing protein purity, potency and quality in an on-demand production system for point-of-care delivery

ABSTRACT

The present invention relates to cell free protein manufacturing, and more particularly, for integrating machine learning into a portable cell-free bioprocessing system for producing proteins with increased and consistent purity, potency and quality wherein such proteins are prepared on-demand and for point-of-care delivery.

CROSS REFERENCE TO RELATED APPLICATION

This application is filed under the provisions of 35 U.S.C. § 371 andclaims the priority of International Patent Application No.PCT/US2019/032350 filed on May 15, 2019 which in turn claims priority toU.S. Provisional Application No. 62/671,566, filed on May 15, 2018, thecontents of all is hereby incorporated by reference herein for allpurposes.

GOVERNMENT RIGHTS IN INVENTION

This invention was made with government support under Grant NumberN66001-13-C-4023 awarded by the Defense Advanced Research ProjectsAgency (DARPA). The government has certain rights in the invention.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to cell free protein manufacturing, andmore particularly, for integrating machine learning into a portable cellfree bioprocessing system for producing proteins with increased andconsistent purity, potency and quality wherein such proteins areprepared on-demand and for point-of-care delivery.

Background of the Related Art

Protein therapeutics, also known as biologics, are currentlymanufactured at centralized facilities according to rigorous protocolscollectively referred to as Current Good Manufacturing Practices (cGMP)(1, 2). Biologics are currently produced in a centralized manufacturingfacility with large scale (>10,000 liters) cell cultures, and with thenecessary large volume separation, purification, formulation, packaging,and distribution infrastructure (e.g. a typical Merck, Pfizer orGenentech plant). The time period from a cell bank to the final deliveryof the therapeutic vial is on the order of 6-8 weeks under idealconditions and produces batches of around 10 Kg bulk protein. Every stepneeds to be individually developed, scaled-up, optimized and validatedin a manufacturing setting. The final product will also have anexpiration date and is either shipped lyophilized or via a cold chain,which must also be documented.

Since such facilities require multiple years to design, build, activate,and qualify, they are unsuited to respond to rapid changes in demand.Furthermore, should a manufacturing facility go offline, as in the eventof a natural disaster, it is likely to result in severe shortages thatwould adversely impact public health, as has happened in Puerto Rico.Further, the pharmaceutical industry of today suffers from enormousexpenditures that nonetheless result in relatively few new drugs andtherapies being introduced to the market (3).

The availability of biologics for treatment of patients innon-conventional healthcare settings, such as combat zones, remote areasof the world, or during natural disasters is limited by the need forextensive manufacturing facilities and transport via cold chain throughpotentially disrupted infrastructure. Further, planning for the exactnature and amount of biologics necessary in a constantly-changingemergent setting is difficult. The critical need for a nimble, portableplatform for manufacture of any needed therapeutic biologic forimmediate point-of-care administration to patients regardless oflocation was originally articulated by the Defense Advanced ResearchProjects Agency (DARPA) specifically for use on the battlefield.Biologically-derived Medicines on Demand (Bio-MOD) was developed inresponse to this challenge and funded by showing the biologicsmanufacturing at the point-of-care (4-7).

The idea of compounding a drug requested by a doctor for production atthe bedside is already being tried in a hospital setting usingconventional cell culture manufacture of biotherapeutics. Such anapproach appears to have the potential to circumvent lengthy regulatoryapprovals, as the biologics would be made under prescription for aparticular patient and would be regarded as a form of compounding.However, there are downsides because of lack of consistency and/orpotency.

Thus, there is a need for production of biological medicines inreal-time and/or on-demand to provide therapeutic proteins in hospitalsor remote locations. Also, there is a need for incorporation ofin-process testing, statistical analysis, sterility/potency validationand feedback mechanism for quality assurance to reduce reliance onexpensive laboratory testing equipment in lab settings. As such, thepresent invention provides for additional detailed characterization ofindividual lots to demonstrate the rigor, consistency and robustness ofcell-free real-time biomanufacturing by integration of on-boardanalytics to a machine learning driven approach that has the potentialto bring exceptional regulatory rigor to the process.

SUMMARY OF THE INVENTION

The present invention provides for an integrated, portable and compactbioprocessing system and method for the production of proteins withbuilt-in confirmation of purity and consistency of produced protein.

In one aspect, the present invention provides a portable and compactcell-free bioprocessing system for the production of on-demandsynthesized protein for point-of-care delivery, the system comprising:

-   -   a. a protein expression module for producing the on-demand        synthesized protein; and    -   b. a protein purification module for purification of the demand        synthesized protein, wherein each module is associated with        on-board analytics and wherein the on-board analytics are        integrated to a machine learning system to analyze properties of        the bioprocessing system during the production and synthesis of        the protein to provide data on the purity, potency and quality        of the on-demand synthesized protein.

In the present invention, the protein expression module comprises atleast one dialysis cassette or reactor for inclusion of cell lysate,reaction mixture and DNA or mRNA for production of the on-demandsynthesized protein. The cell lysate may be from CHO cells or E. colicells. Importantly the lysate can be combined with a buffer in a mixerdiscussed further herein. Besides the cell lysate other reactioncomponent are include in the production module such as amino acids,nucleotides, co-factors, enzymes, ribosomes, tRNA, polymerases andtranscriptional factors. Still further, the reaction mixture may includecomponents selected from the group consisting of salts, polymericcompounds, cyclic AMP, inhibitors for protein or nucleic acid degradingenzymes, inhibitors or regulators of protein synthesis,oxidation/reduction adjusters, non-denaturing surfactants, and buffercomponents.

The purification module comprises a metal ion affinity chromatographycolumn for initial purification and an ion-exchange column for apolishing step of the expressed target protein. Further the purificationmodule may comprise a multiplicity of programmable syringe pumps, UVsensors and pressure sensors to monitor the two-step purificationprocess.

The on-board analytics comprise multiple sensors for collecting dataduring the production process to be analyzed by a machine learningsystem, including but not limited to a cloud based system or anintegrated physical server connected to the system or a near-by serverwith access through a smartphone. The multiple sensors are used tomeasure for dissolved oxygen, pH, absorbance, pressure and temperature.Preferably, the machine learning system uses a blind source separation(BSS) algorithm such as independent-component analysis (ICA) thatextracts independent source signals when the source signals are activesimultaneously and is a BSS algorithm depending on using the ArtificialNeural Networks.

In another aspect of the present invention provides for a portable,cell-free bioprocessing system for on-site synthesis and delivery of anexpressed target protein and verification of purity and consistency ofthe expressed target protein, the system comprising:

-   -   a. a protein expression module wherein the protein expression        module comprises at least one dialysis cassette including cell        lysate, reaction mixture and DNA or RNA for a target protein;    -   b. a purification module, wherein the purification module        comprises a metal ion affinity chromatography column for initial        purification and an ion-exchange column for a polishing step of        the expressed target protein; and    -   c. an artificial intelligence (AI) machine learning module to        collect and store data of real-time testing of the expressed        target protein to provide an output comparison to previously        prepared proteins in the system.

In yet another aspect, the purification module comprises a multiplicityof programmable syringe pumps (2 to 6), at least two UV sensors fromabout 3 to 5 pressure sensors (preferably 4) to monitor the two-steppurification process, which uses an immobilized metal ion affinitychromatography (IMAC) column as a first step, and an ion-exchange resincontaining positively charged groups, such as diethyl-aminoethyl groups(DEAE) column for the second (polishing) step. The pumps operate in thepressure range of 0.2-30 psi and dispense at a rate of 0.004 to 3.0 mlmin⁻¹. Importantly, the system is designed to provide flexibility andcompatibility, allowing for customization of the script, columns,buffers and flow rates according to the requirements of the user.

In a further aspect, the present system provides for data collection tobe analyzed by machine learning to provide a fingerprint profile foreach batch of a specific protein thereby providing information onproduct quality and potency of the produced protein thereby replacingoff-line analysis tools such as NMR and Mass spectroscopy. The data isextracted from each run, collected and analyzed by a mathematical orcomputational model for informational processing. Output date providestatistical analysis on the properties of the produced protein whencompared to previous inputs thereby providing a fingerprint of theproduced protein relative to previously produced proteins.

In yet another aspect, the present invention provides for a method ofanalyzing the purity and quality of a protein produced in a portable,cell-free bioprocessing system for on-site synthesis and delivery of anexpressed protein, the method comprising:

-   -   a. providing pressure and UV sensor data during the production        process for the produced protein;    -   b. transmitting the data to a computer aided classification        system;    -   c. extracting features from the data with the computer aided        classification system for classifying the protein and process        conditions, wherein extracted features characterize the produced        protein and such sample characterization is compared to        characterization of previously extracted features to provide        classified features of the produced protein;    -   d. applying an unsupervised clustering process to the classified        features to provide a plurality of output clusters to provide        enhanced identification of the produced protein.

In another aspect, the present invention provides for a portable systemand method for on-demand production of a therapeutic protein, whereinthe therapeutic protein exhibits increased potency due to the timelysynthesis and substantially immediate delivery of protein. Preferably,the newly synthesized proteins are delivered to a patient within onehour, to one day, to two weeks. Preferably any refrigeration is at atemperature above freezing from 0 to 6° C. Any freezing of the proteinsis preferably a single event with temperatures ranging from about −2° C.to about −10° C.

In yet another aspect, the present invention provides for a portablesystem and method for on-demand production of a protein, wherein theproduced protein can be delivered continuously or as a bolus as it isproduced and as it happens physiologically, that being, where the bodyproduces needed proteins over an extended time in vivo and when needed.

To achieve at least the above aspects, in whole or in part, there isprovided a bioprocessing system comprising a production module forproducing a protein, a purification module for receiving the proteinfrom the production module for purifying the protein from reagents andan artificial neural network for providing data on the produced proteinrelative to previously produced proteins. The bioprocessing system mayfurther comprise a processor for controlling and/or monitoring at leastthe production module and/or the purification module. The processor iscommunicatively connected to at least the production module and/orpurification module to control the timing, temperature and otherparameters necessary for optimizing the production and purification ofthe synthesized proteins to provide a sufficient amount of or atherapeutic dosage of the synthesized protein. Such length of time inthe production module and/or purification module may be used to affectthe potency and/or activity of the synthesized protein and such data iseasily collected and process with the included access to the artificialneural network.

The system may further comprise the use of a smartphone in the analysisprocess. The numerical or analysis data is easy transferred to a smartphone app, transferred through a smartphone to a server that has aprogram to evaluate the data and further processing of a classificationof data methods by an artificial intelligence algorithm with a finalintegration of data into an output that is transferred back to the userand system. The process involves the association of data of the testingresults and outputs for final review that can include the visual data inbar graphs, frequency graphs, and/or audio signals.

Additional advantages, aspects, and features of the invention will beset forth in part in the description which follows and in part willbecome apparent to those having ordinary skill in the art uponexamination of the following or may be learned from practice of theinvention. The aspects and advantages of the invention may be realizedand attained as particularly pointed out in the appended claims.

BRIEF DESCRIPTION OF THE FIGURES

FIGS. 1A-1D show the Biologically-derived Medicine on “Bio-MOD” DemandSystem. FIG. 1A is a photograph of the suitcase-sized system and theactual components with dimensions; FIG. 1B shows a process schematicwherein the system has access to a machine learning system to evaluatethe end product; FIG. 1C shows the schematic of the single-useexpression and 2-step (affinity and ion-exchange) purification device ofthe present invention. The components shown in FIG. 1C includeincubator/shaker holding the cassette (10,000 MWCO bioreactor);temperature-controller; robotic syringe pumps (i-v) which dispense thelysate and buffers: (i) 10-mL syringe, (ii-v) 60-89 mL syringes; steadylysate extractor or holder for cassette reactor; 5 μm syringe filter(Millex®-SV PVDF membrane, Merck Millipore Ltd., Cork, Ireland);microfluidic mixer developed in-house; two-way pinch valves (shown as a,b, and c); 1 mL His-Pur™ Co affinity column (ThermoFisher Scientific,Rockford, IL); UV sensor #1: in-line stainless steel standard flow cellC (part #79853-60000 in the Agilent 1050 variable wavelength detector#79853C). The cell has a pressure rating of 40 bar, a path length of 8mm, and a volume of 14 μL. The built-in sensor uses Seti UVTOP TO18 LEDsat wavelengths 260 nm and 280 nm for dual wavelength light source, andThor labs FGA71 photodiode for the detector. The board iscustom-designed, utilizing a Texas Instruments MSP430F4x micro100controller; 5 mL HiTrap™ DEAE desalting polishing column (GE HealthcareBio-Sciences, Pittsburgh, PA); UV sensor #2 identical to UV sensor #1;polished sample collection compartment; waste; tabletcomputer/controller for collecting information and comprising a machinelearning system (not shown); and FIG. 1D shows the microfluidic mixer ofFIG. 1C showing the picture of the mixer showing placement of the inletand outlet and the basic mixing design with mixing profile.

FIGS. 2A-2C show GCSF-His produced in the Bio-MOD. FIG. 2A shows UV1traces showing the first stage (affinity column) purification; FIG. 2Bshows UV2 traces showing the second stage (polishing column)purification. Box corresponds to the product collection window for runs070, 071, and 073, dashed box run 067 is the blank); FIG. 2C showscorresponding silver stained SDS-PAGE. Average purity was 98%. Theactivity obtained was 0.74±0.04×10⁸ IU/mg, which is in the range ofNeupogen's label activity of 1.0±0.6×10⁸ IU/mg (or 0.4 to 1.6×10⁸IU/mg).

FIG. 3 shows comparison of GCSF-His produced in two identical Bio-MODs.(A) shows UV traces from purification runs on two identical Bio-MODdevices; (B) shows a magnified view of the detected affinity (dashedline window) and polishing (solid line window) elution peaks; 100 μLpolishing fractions were collected from within the detected polishingcollection window;

FIG. 3 Cont. (i) shows (C) and (D) showing the corresponding pressureprofiles from the integrated pressure sensors measuring the pressure atthe back of each of the Bio-MOD syringe pumps; FIG. 3 Cont. (ii) shows(E) and (F) showing the corresponding silver stained SDS-PAGE. Each lanewas loaded with 20 μL of samples taken from 100 μL fractions of polishedsamples collected in the polishing window.

FIG. 4A shows the verification of expression of Ranibizumab in E. coliexpression host; Lane 1: Protein marker—Blue-green color 25KD, Lane 2:Standard, Lane 3 to 6: Rani clone 1, 2, 3, 4 respectively; FIG. 4B showsthe HPLC data confirming expression of light and heavy chain ofRanibizumab.

FIG. 5A shows the components and process schematic of the single-useparts in the current Bio-MOD. All materials in contact with the processhave been validated for leachables and extractables; FIG. 5B providesand explanation of each component; FIG. 5C shows how the process traincan be modified to operate as a continuous production platform, therebyallowing continuous manufacturing to be operationalized.

FIG. 6A shows a waveguide with dissolved oxygen and pH sensors designedto be inserted into dialysis cassette reactor; FIG. 6B shows theconsistency of DO and pH measurements in lysate in three independentminibioreactors. Arrow shows time of DNA addition.

FIG. 7A show a CAD design of multiple column geometries; FIG. 7B-7C showmulti uniform columns made up of three layers of polymethyl methacrylate(PMMA); bottom base plate layer (each 1 mm thick), middle channel layerand top inlet/outlet layer (1.5 mm thick). The top layer contains alarger circular slot towards the outlet for PTFE frits. PTFE frits wereadded post bonding. This array consists of 5 columns of 100 μL volumeand FIG. 7C shows customizable microscale column device (μCol) an arrayof columns with varied resin capacities (25-200 μL, from left to right)displaying the versatility of this; FIG. 7D shows an integrated mixer,capture and polishing column; FIG. 7E shows the Silver stain data,captured using 25 UL Cobalt micro column and polished using 500 μL microcolumn of the column in FIG. 7D; FIG. 7F shows alternative continuousprocessing schemes.

FIG. 8A shows chromatofocusing as a capture step for tag-less G-CSFexpressed in E. coli lysate. Column: 0.7 mL Super Q Buffer A(Loading/wash): 10 mM MOPS, 10 mM Bicine, pH=7.90; FIG. 8B shows theanalysis of the collected fractions. Lane 1, molecular weight marker;lane 2, G-CSF Standard (50 ng); lane 3, G-CSF Standard (150 ng); lane 4,G-CSF Standard (200 ng); lane 5, impurities; lane 6, purified tag-lessG-CSF (1 μL); Lane 7, purified tag-less G-CSF (35 μL); FIG. 8C shows theresults of analytical Size Exclusion Chromatography (SEC) of thepurified tag-less G-CSF.

FIG. 9 shows the real-time multi-parametric sensor with absorbance, CD,fluorescence and lifetime measurement.

FIG. 10A shows result of using the ANN method wherein the top fourprincipal component weights were extracted and input into a 2-layer feedforward ANN with a 10 hidden neuron layer; FIG. 10B (i) and (ii) showsthe product purity; FIG. 10C shows attributes of the corresponding runswhere binned into three ranges: (output layers p₁, p₂, p₃) greater than99%, between 98% and 99%, and less than 98%; FIG. 10D shows the highcorrelation of the ANN fit; FIG. 10E is a schematic of the stepsinvolved in the production of proteins including the use of machinelearning.

FIG. 11A shows G-CSF-His produced in the Bio-MOD, wherein the UV1 tracesshows the first stage (affinity column) purification. Shaded areascorrespond to the different stages: column loading (green), salt wash(blue) and product elution (pink); FIG. 11B shows UV2 traces showing thesecond stage (polishing column) purification. Shaded area corresponds tothe product collection window (for runs 070, 071 and 073; 067 is theblank); FIG. 11C shows the corresponding silver-stained SDS-PAGE; FIG.11D shows Western blot using anti-G-CSF antibody. The higher molecularweight band is due to aggregation after storing the samples for severaldays at neutral pH; FIG. 11E shows the bioactivity of G-CSF-His runs070, 071 and 073 along with blank run 067. Negative control samples wereinactivated by boiling at 100° C. for min. The measures of centre anderror bars represent the means and s.e.m. for n=3.

FIG. 12 shows the characterization of GBP and EPO produced by Bio-MOD,FIG. 12A shows the fluorescence spectra of the acrylodan-labelled GBPshowing quenching of fluorescence in the presence of glucose; FIG. 12Bshows the binding isotherm for glucose in GBP; FIG. 12C shows the ELISAassay of harvest and purified fractions of EPO run 085. The measures ofcentre and error bars represent the mean and s.e.m. for n=3.

DETAILED DESCRIPTION OF THE INVENTION

The Biological derived Medicines on Demand (Bio-MOD) system, as shown inFIGS. 1A-1D, is designed for the production of a variety of therapeuticproteins in single or multiple small doses whenever and wherever theyare needed. These point-of-care settings may range from the patient'sbedside, a doctor's office, a local pharmacy, the battlefield, disasterareas, or very remote locations. The Bio-MOD device produces theseproteins using in vitro translation (IVT) (8,9) (also called cell-freeprotein synthesis) where cell lysates are used to rapidly expressproteins rather than intact living cells. The goal of the Bio-MODtechnology is to combine IVT from lyophilized cell lysates withmicrofluidic purification methods to produce highly purified products ina few hours using an automated platform with built-in diagnosticsincluding the use of machine learning to monitor process consistency.Essentially, this manufacturing technology can be compared to a GMPfacility in a box.

The major advantages and focus of the present invention are centered onpatient safety and the incorporation of in-process testing, statisticalanalysis, sterility/potency validation and feedback mechanism forquality assurance. As such, the present invention provides foradditional detailed characterization of individual lots to demonstratethe rigor, consistency and robustness of cell-free real-timebiomanufacturing. A major advance of the disclosed point-of-caremanufacturing approach is the integration of on-board analytics to acloud-based, machine-learning CMC (Computational Medicine Center)approach to provides needed governmental quality assurance.

The system is designed to have modular components to provide flexibilityand compatibility, allowing for customization of the script, columns,buffers and flow rates according to the requirements of the user. Thesoftware controls the device through a conventional USB interface, wherethe overall power requirement is less than 90 W. The current system is astand-alone deployable unit that can operate for up to three end-to-endcycles of protein production per day onsite. The inherent long-termstability of the lyophilized IVT components makes them ideal foron-demand and on-site protein production and freedom from a cold-chain.

The Bio-MOD 3.0 systems has five programmable syringe pumps, two UVsensors and four pressure sensors to monitor the two-step purificationprocess, which uses an immobilized metal ion affinity chromatography(IMAC) column as a first step, and an ion-exchange (DEAE) column for thesecond (polishing) step. The pumps operate in the pressure range of0.2-30 psi and dispense at a rate of 0.004 to 3.0 ml min−1. Withstandard biocompatible, disposable connectors and fluid flowrestrictors, the bioprocess fluid train was tested to withstand up to˜30 psi during operation. An off-the-shelf, single-use 1 ml IMAC columnand a 5 ml DEAE column comprise the current purification scheme. Thesystem is designed to have modular components to provide flexibility andcompatibility, allowing for customization of the script, columns,buffers and flow rates according to the requirements of the user. Thesoftware program (written in LabVIEW) consists of a user interface toselect either a preloaded or a customized script, which initiates a run.A single button push initiates the entire operation from priming of thefluid train to collection of the purified protein in a sterile vial, intheory, ready for immediate administration to the patient. A dashboardis available to monitor the various sensor data in real time, which arelogged into a file for data collection and post-run analysis. Thesoftware controls the device through a conventional USB interface. Theoverall power requirement is <90 W. The system is a stand-alonedeployable unit that can operate for up to three end-to-end cycles ofprotein production per day onsite. Interchangeable process analyticaltechnology (PAT) has been implemented as plug-and-play sensors forin-line absorbance, pressure and temperature sensors. With PAT, theBio-MOD incorporates self-monitoring software through all phases of theBio-MOD set-up and purification, making the device simple and userfriendly even for non-experts.

For set-up and priming of the fluid train, the user commences a simpleauto-priming procedure where the Bio-MOD monitors the flow pathconfirming that the priming is performed properly and free of bubbles.Depending on the desired purification process, three to four interactivecheck points (depending on choice of purification scheme) make the useraware of any problems with leaks or bubbles in the priming of the fluidtrain, and aid in identifying a quick fix such as increasing the primingcycle to flush the system. All purification system parameters such asbuffer conditions, column residence times and flow rates were initiallyoptimized using a standard Dionex Ultimate 3000 HPLC system (ThermoFisher Scientific, Bannockburn, IL). These parameters were thentranslated to the automated Bio-MOD system.

Cell-free protein (IVT) synthesis offers a major paradigm shift for theproduction of biopharmaceuticals. Traditional biotechnology employsmillions of miniscule bioreactors, the cells, distributed sparselythroughout the macroscopic bioreactor, typically at 5-10% of the totalvolume. Biosynthetic components are condensed at high concentrationswithin these individual chambers. With cell-free approaches, thesecatalytic components become distributed evenly throughout the entirereactor volume, but typically at 5-10% of intracellular concentrations.Although the resultant biopharmaceutical volumetric productivities aresimilar, tremendous advantages are gained because now all metabolicresources can be focused on producing a single protein, instead of atleast several hundred, and importantly access to the actual reactionchamber.

Protein folding is more effective because only a single protein is beingproduced and because the folding environment can be customizedspecifically for that product. Further, because the ribosomes are spacedfarther apart, the risk that the emerging polypeptides willinappropriately interact is reduced. Because translational elongationfactors are diluted, polypeptides also emerge more slowly from theribosomes so that co-translational folding pathways are encouraged.Finally, direct access to the translation and folding environment allowsoptimization of foldase and chaperones concentrations as well asadjustment of solution properties such as the ionic strength and —SH/S—Sredox potential. For example, the production of proteins that are poorlysoluble or suffer from a hydrophobically-mediated kinetic trap in theirfolding pathway can be accumulated to much higher concentrations usinglower ionic strength reaction mixtures. Also, proteins with multipledisulfide bonds can often be folded more effectively by optimizing the—SH/S—S redox potential and protein disulfide concentration. Finally,folding is often improved by customizing the chaperones type andconcentration. Most of these measures are not possible at all or aredifficult to implement with cell-based production.

In recent studies, expression of proteins using freeze-dried cell-freeextracts has been described for portable production of peptides andvaccines (10, 11), as well as the conversion of digital sequences tonucleotide sequences using an automated DNA synthesizer and liquidhandling system (12). The present invention exceeds these efforts ofrapid protein expression with the incorporation of highly effectiveprotein purification into a portable system, as well as onboard qualitycontrol through the use of machine learning. The result is themanufacture of a pure and potent biologic ready to be dispensed at thepoint-of-care. In addition, the DNA synthesizer described in (12) can beplugged into the Bio-MOD system for protein manufacturing starting withdigital sequences.

The Bio-MOD platform shown in FIGS. 1A, 1B and 1C has two modules: theprotein expression module and the protein purification module, each withassociated analytics. The hardware is designed in two parts with fixedhardware (pumps, sensor, tablet computer) and a single-use bioprocesstrain (reactor, syringes, tubing, microfluidic mixers, capture andpolishing columns). The system is fully automated with built-in softwareand programmable syringe pumps with pressure sensors for the delivery oflysate and buffers. Protein expression is currently carried out indialysis cassettes. Once loaded with the IVT reaction, which is composedof the cell lysate, reaction mix and cDNA for the target protein, thecassette is immersed in dialysis buffer inside a sealed bag, hereaftercalled the “reactor.”

A quick review of protein synthesis is provided herein where a proteinis expressed in three main steps: replication, transcription andtranslation. DNA multiplies to make multiple copies by a process calledreplication. Transcription occurs when the double-stranded DNA isunwound to allow the binding of RNA polymerase producing messenger RNA(mRNA). Transcription is regulated at various levels by activators andrepressors, and also by chromatin structure in eukaryotes. Inprokaryotes, no special post-transcriptional modification of mRNA isrequired. However, in eukaryotes, mRNA is further processed to removeintrons (splicing), to add a ‘cap’ (M7 methyl-guanosine) at the 5′ endand to add multiple adenosine ribonucleotides at the 3′ end of mRNA togenerate a poly(A) tail. The modified mRNA is then translated.

The translation or protein synthesis is also a multi-step process withInitiation, Elongation and Termination steps and is similar in bothprokaryotes and eukaryotes. The difference is that in eukaryotes,proteins may undergo post-translational modifications, such asphosphorylation or glycosylation. The translation process requirescellular components such as ribosomes, transfer RNAs (tRNA), mRNA andprotein factors as well as small molecules like amino acids, ATP, GTPand other cofactors.

The difference between in vivo and in vitro (cell-free) proteinexpression is that in cell-free expression, the cell wall and the nucleiare no longer present. To obtain the cell extract for cell-free proteinexpression, cells (E. coli, wheat germ, mammalian cells) are subjectedto cell lysis followed by separation of the cell wall and nuclear DNA.The desired protein is synthesized by adding a DNA or mRNA template intothe cell extract together with a reaction mix comprising of biologicalextracts and/or defined reagents. The reaction mix is comprised of aminoacids, nucleotides, co-factors, enzymes and other reagents that arenecessary for the synthesis, e.g. ribosomes, tRNA, polymerases,transcriptional factors, etc. When DNA is used as template (i.e. linkedreaction), it is first transcribed to mRNA. Alternatively, mRNA couldalso be used directly for translation.

The template for cell-free protein synthesis can be either mRNA or DNA.Translation of stabilized mRNA or combined transcription and translationconverts stored information into a desired protein. The combined system,generally utilized in E. coli systems, continuously generates mRNA froma DNA template with a recognizable promoter. Either endogenous RNApolymerase is used, or an exogenous phage RNA polymerase, typically T7or SP6, is added directly to the reaction mixture. Alternatively, mRNAcan be continually amplified by inserting the message into a templatefor QB replicase, an RNA dependent RNA polymerase. Purified mRNA isgenerally stabilized by chemical modification before it is added to thereaction mixture. Nucleases can be removed from extracts to helpstabilize mRNA levels. The template can encode for any particular geneof interest.

Salts, particularly those that are biologically relevant, such asmanganese, potassium or ammonium, may also be added. The pH of thereaction is generally run between pH 6-9. The temperature of thereaction is generally between 20° C. and 40° C. These ranges may beextended.

In addition to the above components such as cell-free extract, genetictemplate, and amino acids, other materials specifically required forprotein synthesis may be added to the reaction. These materials mayinclude salts, polymeric compounds, cyclic AMP, inhibitors for proteinor nucleic acid degrading enzymes, inhibitors or regulators of proteinsynthesis, oxidation/reduction adjusters, non-denaturing surfactants,buffer components, spermine, spermidine, etc.

The salts preferably include potassium, magnesium, ammonium andmanganese salts of acetic acid or sulfuric acid, and some of these mayhave amino acids as a counter anion. The polymeric compounds may bepolyethylene glycol, dextran, diethyl aminoethyl dextran, quaternaryaminoethyl and aminoethyl dextran, etc. The oxidation/reduction adjustermay be dithiothreitol (DTT), ascorbic acid, glutathione and/or theiroxides. Further DTT may be used as a stabilizer to stabilize enzymes andother proteins, especially if some enzymes and proteins possess freesulfhydryl groups. Also, a non-denaturing surfactant such as TritonX-100 may be used at a concentration of 0-0.5 M. Spermine and spermidinemay be used for improving protein synthetic ability, and cAMP may beused as a gene expression regulator.

The current onboard protein expression module is equipped with a Peltierheating source, temperature sensor, and muffle-fan for a uniform andcontinuous distribution of heat. Both the temperature and shaking speedare programmable via LabVIEW software program using a computer tablet.The protein purification module has two built-in flow cells, eachequipped with a UV sensor for monitoring the 2-step purification processinvolving affinity chromatography and ion-exchange chromatography forpolishing. In addition, four pressure sensors are incorporated behindeach syringe pump plunger for continuous system pressure monitoring.These along with system temperature form the current process analyticaltechnologies (PAT) sensors that are already proving their value indemonstrating process consistency.

FIG. 1C shows the Bio-Mod system has five programmable syringe pumps,two UV sensors and four pressure sensors to monitor the two-steppurification process, which uses an immobilized metal ion affinitychromatography (IMAC) column as a first step, and an ion-exchange (DEAE)column for the second (polishing) step. A dedicated miniaturized shakerthat is closer in speed to the standard benchtop is used for integrationinto the system. The pumps operate in the pressure range of 0.2-30 psiand dispense at a rate of 0.004 to 3.0 ml min−1. With standardbiocompatible, disposable connectors and fluid flow restrictors, thebioprocess fluid train was tested to withstand up to −30 psi duringoperation. An off-the-shelf, single-use 1 ml IMAC column and a 5 ml DEAEcolumn comprise the purification scheme. The system is designed to havemodular components to provide flexibility and compatibility, allowingfor customization of the script, columns, buffers and flow ratesaccording to the requirements of the user. The software program (writtenin LabVIEW) consists of a user interface to select either a preloaded ora customized script, which initiates a run. A single button pushinitiates the entire operation from priming of the fluid train tocollection of the purified protein in a sterile vial, in theory, readyfor immediate administration to the patient. A dashboard is available tomonitor the various sensor data in real time, which are logged into afile for data collection and post-run analysis. The software controlsthe device through a conventional USB interface. The overall powerrequirement is <90 W. The Bio-MOD system is a stand-alone deployableunit that can operate for up to three end-to-end cycles of proteinproduction per day onsite. Interchangeable process analytical technology(PAT) has been implemented as plug-and-play sensors for in-lineabsorbance, pressure and temperature sensors. With PAT, the Bio-MODincorporates self-monitoring software through all phases of the Bio-MODset-up and purification, making the device simple and user friendly evenfor non-experts. For set-up and priming of the fluid train, the usercommences a simple auto-priming procedure where the Bio-MOD monitors theflow path confirming that the priming is performed properly and free ofbubbles. Depending on the desired purification process, three to fourinteractive check points (depending on choice of purification scheme)make the user aware of any problems with leaks or bubbles in the primingof the fluid train, and aid in identifying a quick fix such asincreasing the priming cycle to flush the system.

All purification system parameters such as buffer conditions, columnresidence times and flow rates were initially optimized using a standardDionex Ultimate 3000 HPLC system (Thermo Fisher Scientific, Bannockburn,IL). These parameters were then translated to the automated Bio-MODsystem.

The components and process schematic of the single-use parts in thecurrent Bio-MOD are clearly defined in FIG. 5A. FIG. 5B providesclarification on all respective parts. Cost of disposable train<$500.FIG. 5C shows how the process train can be modified to operate as acontinuous production platform, thereby allowing continuousmanufacturing to be operationalized.

Such Bio-MOD system has been thoroughly validated and demonstrated forproof of operation with several proteins (7) and as discussed below.Importantly, all materials in contact with the process have beenvalidated for leachables and extractables.

Maximizing yields from the expression system is important and currently,most cell-free expression is carried out in batch mode, but continuouscell-free expression is also possible. Improvements to achieve thehighest possible yields and purification recoveries of >70% overall ofthe final product can be accomplished by rocking the chamber to improvemixing and mass transfer (4). Notably, critical process parameters (pH,DO, temperature) are considered to be important for monitoring duringthe expression and thereby adding another layer of robustness during theprocess monitoring. For example, use of a dialysis cassette withintegrated sensors as part of the expression module. Further,optimization of the Bio-MOD can be achieved by co-expression ofchaperones, addition of amino acids, energy substrates etc, as recentlydescribed in (4, 5, 7, 13).

The automated production of the proteins in the Bio-MOD system wasimplemented for the protein expression of G-CSF (typically 2.0 ml) andseveral other proteins described later herein were executed in a 10 or20 kDa MWCO dialysis cassette and incubated in a Sartorius incubator at30° C. and 150 r.p.m. for 6 h. The cassette containing the harvest wasremoved from the incubator and placed in the purification module, Pump Iwas programmed to withdraw 2.1 ml from the dialysis cassette to ensureall of the 2.0 ml harvest is collected. The harvest was passed through a5 μm filter to remove protein aggregates before reaching thein-house-developed microfluidic mixer (FIG. 1C), where the harvest wasmixed with the loading/binding buffer. By simultaneously operating bothpump I and pump II at 0.2 ml min⁻¹ pushing fluids towards themicrofluidic mixer, the harvest was diluted five times with bindingbuffer in a continuous manner. A unidirectional check valve was employedto prevent backflow of the harvest or the binding buffer from the flushlines. To maximize product recovery, the filter and fluidics were rinsedwith 1 ml binding buffer. The diluted harvest was loaded onto apre-saturated 1 ml HisPur Cobalt column at a rate of 0.2 ml min⁻¹ tomaximize product capture. After that, the column was washed with 1×PBSby operating pump II. A second wash with a high salt concentration usingpump III removed a significant portion of impurities in the affinitycolumn due to non-specific binding. The product was eluted from thecolumn by switching to pump IV, which dispensed the elution buffer. Theentire purification process was monitored in real time by measuring theUV absorbance at 280 nm. This was achieved by positioning a standardHPLC flow cell fitted with a custom-made UV sensor after the affinitycolumn. The sensor is comprised of an LED as a UV light source and aphotodiode to detect the transmitted light, along with custom circuitboards to control these components. The UV sensor is integrated with thesoftware module allowing feedback-based control of other components suchas the two-channel pinch valves.

The process algorithm detects the change in slope of the UV trace duringelution of the product. With the addition of a polishing step, theelution peak from the affinity column was automatically directed to apre-saturated 5 ml HiTrap DEAE fast flow column (GE cat. no.GE17-5154-01). Pump V dispenses the polishing buffer (20 mM phosphatebuffer with 50 mM arginine) at 1 ml min⁻¹ through the ion-exchangecolumn. G-CSF-His is not retained in the DEAE column (product is presentin the void volume). Thus, the start and end points of productcollection were automatically calculated based on the post-UV flow cellvolume, tube length and flow rate, ensuring precise control of the pinchvalve. Finally, the polished product was collected in a vial for furtheroff-line analysis. Three independent runs (runs 070, 071 and 073) toproduce G-CSF-His were done with a corresponding negative control (run067). The entire time needed for the end-to-end production run includingprotein expression and purification was about 8.5 h. Traces from UVsensor 1 (FIG. 11A, affinity column) and UV sensor 2 (FIG. 11B,polishing column) of G-CSF-His produced in the Bio-MOD have very similarprofiles.

The quality and activity of the G-CSF-His was characterized offline.Purity was found to be approximately −98% as determined by highsensitivity silver stained SDS-PAGE (FIG. 11C and Table 1), with a clearimprovement in results due to polishing. This level of purity conformsto that typically expected by regulatory agencies for investigationaltrials of parenteral biologics (https://go.nature.com/21zJyYD).Increased purity beyond this level can be achieved by incorporatingadditional polishing steps using strategies similar to those employedfor biologics produced in cell-based expression systems. Additionally,the high-sensitivity silver-stained SDS-PAGE showed consistent purityand concentration at the end of the ion-exchange purification forG-CSF-His (runs 070, 071 and 073). An average yield of ˜110 μg wascalculated based on the integrated signal density of the protein bandsin the silver stains. These values were used in the determination ofactivities in FIG. 11E and Table 1 (shown below).

The identity of the IVT-expressed G-CSF samples was confirmed by westernblot analysis using anti-G-CSF antibody showing a product band at ˜19kDa which is slightly higher than the native (standard) G-CSF band at˜17 kDa. This was expected due to the presence of the 6×His tag and theadditional amino acids from the IVT vector (‘M-A-T-T-H’ at theN-terminal and ‘L-E’ preceding the His-tag sequence). The activity ofthe Bio-MOD derived human G-CSF-His was queried using a standard cellproliferation bioassay, with the results showing that the activity ofthe Bio-MOD-produced G-CSF-His is at an order of magnitude higher thanthe single-step purified G-CSF-His. The activity of polished G-CSF-Hisproduced using the Bio-MOD system (FIG. 11 E and Table 1) is on par withthat of the originator molecule, Amgen's Neupogen and the biosimilarZarxio from Sandoz, which have specific activities of 1.0±0.6×10⁸ IUmg−1 (or 0.4 to 1.6×10⁸ IU mg-1) (https://go.nature.com/2K1CRr6). Table1 summarizes the yield, purity and activity of polished G-CSF-Hispurified in the Bio-MOD system.

TABLE 1 Yield, purity and activity of polished G-CSF-His in the Bio-MODYield (μg) Silver-stained Activity (IU Run No. SDS CE-SDS ELISA Purity(%) mg⁻¹) 070 130 + 17 123 + 19 156 + 9  98.0 0.69 ± 0.06 × 10⁸ 071 96 + 14 105 + 35 104 + 7  98.5 1.10 ± 0.04 × 10⁸ 073 108 + 16  77 + 12105 + 11 97.1 0.42 ± 0.03 × 10⁸ Average activity of reference standard(WHO, NIBSC cat. No. 09/136) = (1.0 ± 0.06) × 10⁸ IU mg⁻¹. The measuresof centre and errors represent the mean and s.e.m. of triplicatemeasurements.

G-CSF was chosen to work with for the following reasons: i) it is anapproved therapeutic with several variations on the market; ii)Extensive literature data are available (18-21); iii) it is FDA-approvedfor mitigation of radiation exposure using the animal rule; and iv)significant preliminary data is available on its manufacture in theBio-MOD system (7). As shown in FIG. 2 , G-CSF was produced withacceptable purity and potency with the present Bio-MOD system and wasshown that 770 ug of pure G-CSF was made (adult dose of Neupogen is 300ug). Specifically FIG. 2 shows (A) UV1 traces showing the first stage(affinity column) purification and (B) UV2 traces showing the secondstage (polishing column) purification. Box corresponds to the productcollection window for runs 070, 071, and 073, dashed box run 067 is theblank). (C) Corresponding silver stained SDS-PAGE. Average purity was98%. The activity obtained was ±0.04×10 8 IU/mg, which is in the rangeof Neupogen's label activity of 1.0±0.6×10⁸ IU/mg (or 0.4 to 1.6×10⁸IU/mg).

Notably, FIG. 3 shows the comparison of G-CSF-His produced in twoidentical Bio-MODs. (A) shows UV traces from purification runs on twoidentical Bio-MOD devices. (B) shows a magnified view of the detectedaffinity (dashed line window) and polishing (solid line window) elutionpeaks; 100 μL polishing fractions were collected from within thedetected polishing collection window. (C) and (D) show the correspondingpressure profiles from the integrated pressure sensors measuring thepressure at the back of each of the Bio-MOD syringe pumps. (E) and (F)show the corresponding silver stained SDS-PAGE. Each lane was loadedwith 20 μL of samples taken from 100 μL fractions of polished samplescollected in the polishing window. In-line conductivity sensors are alsoconsidered for incorporation in the purification module along with otheranalytics, as described herein below.

Additionally, numerous types of lyophilized cell extracts can be used inthe Bio-MOD system, including but not limited to E. coli, Vibrio, CHOand Tobacco (plant) cell extracts for use both batch and continuousmanufacturing approaches. Further, numerous types of purification can beconducted including using a i) His-tag; ii) immobilized metal affinitychromatography (IMAC) followed by Ion exchange chromatography (IEC);iii) tagless intein-based purification; iv) tagless production andpurification using chromatofocusing and polishing with IEC; and v)affinity purification using a G-CSF receptor as the capture agent (thishas the added advantage of serving as a self-referenced potency assay).

As shown above, G-CSF was produced with acceptable purity and potencywith the present Bio-MOD system. In response other proteins have beenproduced including Ranibizumab, also known as Lucentis, which is a 48kDa humanized monoclonal antibody fragment. Normally, this protein isproduced as inclusion bodies in E. coli. In the present invention, thewhole antibody fragment was successfully cloned under the T7 promotersuch that there is a Shine-Dalgarno sequence in between the light andheavy chains for the equal expression of both chains. The nativeRanibizumab gene was accessed from GenBank was synthesized and clonedinto the pET-15b vector. The ligated plasmid-Ranibizumab mixture wastransformed into competent E. coli DH5α cells and selected on Luriabroth (LB) plates containing 100 μg/mL ampicillin at 37° C. The positivetransformants were verified by restriction digestion and sequencing. Thepositive clones were further transformed into BL21(DE3) pLys S cells andtheir expression was confirmed by SDS PAGE and HPLC analysis (shown inFIG. 4 ).

FIG. 4A shows the verification of expression of Ranibizumab in E. coliexpression host. Lane 1: Protein marker—Blue-green color 25KD, Lane 2:Standard, Lane 3 to 6: Rani clone 1, 2, 3, 4 respectively and FIG. 4Bshows the HPLC data confirming expression of light and heavy chain ofRanibizumab. Optimization of media, induction concentration andtemperature required for maximum possible protein production has beendone at shake flask level. The highest protein titre of 0.3-0.4 mg/mLusing HPLC was obtained in optimized modified SOC medium at 37° C. with1 mM IPTG induction concentration.

The cell free expression of G-CSF in the Bio-MOD system demonstrates theability to consistently manufacture pure and potent product and animalstudies have been successfully conducted. Since the Ranibizumab(Lucentis™) Fab antibody fragment is a heterodimer with disulfide bondswithin and between each dimer, the cell-free production of the presentinvention offers special advantages. First, experience suggests thatfolding is improved if some amount of light chain can accumulate beforeheavy chain production begins. This is easily achieved by first addingonly the plasmid with the light chain gene and then, after an optimaldelay, adding the heavy chain expression gene. Secondly, direct accessto the reaction solution allows for adjustment of the —SH/S—S redoxpotential and the disulfide isomerase concentration for optimal folding.In addition, recent experience suggests that adjusting the ionicstrength of the reaction solution can improve protein folding. This maybe especially advantageous since heavy chain binding domains oftenpresent hydrophobic residues. Finally, direct access allows the additionof chaperones (such as Skp and FkpA) at optimal concentrations tofurther improve folding. Such features can be optimized to achieve highLucentis concentrations and product quality.

To demonstrate feasibility of also producing a reagent protein the GBPwas used as a model. GBP is a fluorescent biosensor for micromolarlevels of glucose. The GBP produced in the Bio-MOD (runs 056 and 057)were confirmed by SDS-PAGE and western blot analysis. After offlinelabelling with the fluorescent dye acrylodan, a ˜45% change influorescence at the highest glucose concentration (FIG. 12A) wasobserved, consistent with results using GBP expressed in Escherichiacoli. The binding isotherm shown in FIG. 12B also agrees with GBPexpressed in whole-cell methods.

Post-translational modifications (PTMs) such as glycosylation aregenerally difficult to achieve in IVT due to the absence or decreasedpresence of the necessary organelles found in whole cells. To show thepossibility of glycosylation in a Bio-MOD product, we chose EPO waschosen (40% glycosylated) as a model therapeutic. EPO is a hormone thatstimulates the development of red blood cells and is used to treatanaemia in patients such as those undergoing dialysis. Supplementationof the cell-free lysate with CHO microsomes (30% of the total reactionmixture) resulted in successful glycosylation of EPO-His as evidenced bya shift in molecular weight from 25 to 20 kDa following treatment withpeptide: N-glycosidase F (PNGase). A slowing down of the reaction bydecreasing the reaction temperature and not supplementing the reactionwith GADD34 (see Methods) also contributed to successful glycosylation.Enzyme-linked immunosorbent assay (ELISA) indicated proper proteinfolding after the affinity purification, as shown in FIG. 12C. Inprevious experiments, low levels of glycosylation in EPO were observedin CHO IVT lysate, which proves that the small amount of microsomesalready present in the lysate survive lyophilization. Thus, in thefuture, microsomes can be added to the liquid CHO IVT lysate beforelyophilization. This will ensure full portability of the reaction mixfor the production of glycosylated proteins. The basic design and use ofthe Bio-MOD system presents a miniaturized, rigorous and controlledproduction process that can minimize natural variations associated withprotein expression based on living cells. The software for thepurification procedure in the Bio-MOD incorporates the highest level ofself-monitoring, giving the user the ability to press ‘start’, verifyproper loading, walk away and return to a purified sample. Real-timeself-monitoring has the advantage of being able to distinguish the‘acceptable’ versus the ‘unacceptable’ runs based on specific (UV orpressure) profiles established from multiple runs.

As discussed earlier, the point-of-care protein production platformdescribed herein can use several purification methods. Usually, ahistidine tag has been used for IMAC purification. For additional typesof purification, chromatofocusing is considered an alternativenon-affinity capture method as well as a potential polishing method.Prior work has demonstrated the utility of chromatofocusing for similarapplications (14). Additionally, other options for use in the Bio-MODplatform include periodic countercurrent chromatography, simulatedmoving bed chromatography, and sequential multicolumn chromatography(15,16). By using a microfluidic approach, microcolumns can befabricated and tested in conjunction with both batch and continuouscell-free expression in an optimized format by varying the columnpackings used, as well as the column geometry and interconnections.Design of Experiments (DoE) and Quality-by-Design (QbD) methods can beemployed with the goal of achieving optimal performance and processcontrol by exploiting a better fundamental understanding of theprocesses. Testing of such designs may employ simulations of the Bio-MODsystem using the microfluidic module in COMSOL Multiphysics®, which is afinite element modeling environment currently available. (17). FIG. 6Ashows the inclusion of waveguides for dissolved oxygen and pH sensorswhich are inserted I the dialysis cassette. Notably, FIG. 6B shows theconsistency of dissolved oxygen and pH measurements in three independentminibioreactors. The arrows show time of the DNA addition (5).

FIG. 7 illustrates some of the basic elements for purification including7A. a CAD design; 7B. Multi uniform columns; 7C. Varying capacitycolumns; 7D. Integrated mixer, capture and polishing column; 7E. Resultsfrom G-CSF capture and polishing using it, and 7F showing variouscontinuous processing schemes. Additional steps may include additionalpurification steps prior to cell-free protein synthesis. This includescell lysis and centrifugation steps to remove cell walls and aggregates.Also phase extraction can be applied along with affinity chromatography,and expanded centrifugation steps to better clarify extracts prior touse, simplify purification and decrease the risk of contamination. Inthe event that aggregates pose a problem, an additional size exclusionstep can be added.

The present invention provides for not only a robust real-time releaseof drug to the patient but also in-line (rather than off-line) qualityanalysis of the produced protein with sensors. Real time sensors areincluded for determining product concentration and quality. Such sensorsmay include but not limited to silver stained gels, Labchip, HPLC,Blitz, ELISA, CD, UV-Vis, fluorescence and flow cytometry (potency).Importantly, in the present invention, the extracted data is included ina machine learning system and compared with previous results fromoff-line testing and other on-line testing. The process data forbio-derived medicines can include chromatography data: UV (260-280),fluorescence, pressure, conductivity, light-scatter, CD, etc. Suchuseful feature extraction from process control data is correlated toproduct quality through previous batch runs. The machine learning canthem optimize extraction or choice of features that best describeschanges in the process relevant to product quality.

The characteristics of protein detection and quantitation wereevaluated. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis(SDS-PAGE) analyses is used. Criterion TGX™ precast midi protein gel(4-20%) is used following standard protocol with a Criterion™electrophoresis cell, both obtained from (Bio-Rad, cat. #1656001 and#5671093). For staining gels, ProteoSilver™ plus silver stain kit(Sigma-Aldrich, cat. #PROTSIL2) is used. Known concentrations ofcell-derived glucose binding protein (GBP), G-CSF (Life Technologies) orbovine serum albumin (BSA) are loaded along each sample and used asstandard reference for determining the purity and the concentration ofpurified protein. The area of each band is measured with ImageJ softwareand the concentration calculated relative to the standard curve. Percentpurity is determined using the same image analysis software by takingthe ratio of the area of the known, lowest detectable band vs. the totalarea, where the total area is equal to the area of the lowest detectableprotein band plus area of impurities in an overloaded gel. 660 nm assay.Quantitative protein analysis is done using Pierce 660 nm protein assaykit (Thermo Scientific, Cat. #22660) following manufacturer'srecommended protocol. Western Blot and ELISA. The G-CSF samples arediluted 10× with PBS. 20 μL aliquots and treated with 6 μL of 5×dilutedLaemmli buffer dye, boiled at 100° C. for 5 minutes, then loaded to apre-cast 12.5% tris-HCl gel (Bio-rad, Cat. #3450014) and run at 100V.Samples are then transferred to a nitrocellulose membrane (Bio-rad, Cat.#1620233) and left in 20-mL blocking buffer overnight. Primary antibody(Rabbit anti-G-CSF, Abcam, Cat. #9691) is added at a concentration of1:3000 to 20 mL blocking buffer the next day, removed after an hour, andthe blot is washed with PBST. Fresh blocking buffer (20 mL) is thenadded with a complementary HRP-conjugated secondary antibody (GoatAnti-Rabbit HRP, Abcam, Cat. #ab6721) at a concentration of 1:3000.Solution is removed after 1 hour, and the blot is washed with PB ST.Finally, a chemiluminescent substrate (Thermo Scientific, Cat. #34075)is added to the blot and imaged using a Thermo Scientific myECL™ Imager.The same antibody is used for an ELISA assay. Reversed-phase and sizeexclusion high performance liquid chromatography (RP-HPLC) of G-CSFIMAC-purified samples is analyzed on a Dionex Ultimate 3000 series HPLCsystem (Thermo Fisher Scientific, Bannockburn, IL) using a BioBasic C18column (Thermo Scientific). Mobile phase A consists of 0.1% TFA in waterwhile mobile phase B consists of 0.1% TFA in acetonitrile. Gradientelution of 30-100% B in 25 minutes at a flow rate of 0.5 mL/min is used.

G-CSF is a pleiotropic cytokine that is heavily involved inhematopoietic cell differentiation and function. The activity of G-CSFis studied. Multiple functions of G-CSF are compared directly topurified recombinant protein from commercial vendors by 1) quantifyingJAK2/STAT3 signaling by Western blot and phospho-flow cytometry (18); 2)evaluating the differentiation of G-CSF-treated bone marrow precursorsto granulocytes by flow cytometry, Wright-Giemsa staining forgranulocytes, and gene expression using real-time quantitative reversetranscription PCR qRT-PCR (19, 20 and 3) assaying the polarization oftolerance-inducing blood monocytes by flow cytometry and ELISA (21). Thelatter two in vitro assays are directly related to potential therapeuticuses of G-CSF in the clinic for post-radiation recovery andtolerance-inducing therapies in autoimmunity, respectively.

Additional real-time analytics can also be conducted. For example,real-time sterility testing is conducted for detection of bacterialcontamination using resazurin as indicator (22, 23). The dye is oxidizedby NADH in the cells and converted into highly fluorescent rezorufin.The incubation is performed in a microfluidic chip and is monitoredusing a miniature fluorimeter. The rate of fluorescence increase isproportional to CFU in the sample. The method integrates a negativecontrol to account for oxidation properties of the other possiblereactive substances.

Product concentration is determined with a rapid, microfluidic ELISAtechnique previously used and demonstrated for StaphylococcalEnterotoxin B (SEB) (24-26). In addition, an in-line optical sensor isused for real time fingerprinting and quality assessment of the finalproduct. This is shown conceptually in FIG. 9 and captures data onabsorbance, CD, fluorescence and lifetime measurements. For example,G-CSF is interrogated optically in a flow cell of a UV sensor before itis collected in a vial for delivery to the patient. Absorptioncorrelates with protein concentration and together with intrinsicfluorescence can be used for fingerprinting of the sample and forevaluation of its purity (27). Protein fluorescence is due to tryptophanresidues (λex=280 nm, λem=350 nm) and to a limited extent, tyrosine andphenylalanine. Another fingerprint can be obtained by measuring theintrinsic fluorescence lifetimes. These lifetimes are obtained bymodulating the intensity of the light source and detecting the time lagas well as the amplitude change of the emission using a fast outputdetector. Cross correlation decreases the frequency of operation,permitting the use of simpler and less-expensive optical components.Because tryptophan fluorescence is highly dependent on environment,fluorescence wavelengths and lifetimes can inform on the structure andintegrity of the protein structure (27). Yet another fingerprint can beobtained by in-line measurements of the circular dichroism. This will bedone by keeping the intensity of the UV light source constant, whilemodulating its polarization using an acoustoelastic modulator. CD dataprovides real-time information on protein secondary structure andwhether the protein product is folded correctly. The current off-linemeasurements are used to validate the in-line ones and feed the machinelearning algorithms of the software for in-built PAT.

Process Software and Machine Learning Module:

To provide adequate controls and the need to have statistical analysisof each process and the product produced, machine learning is used inthe Bio-MOD system. Notably, traditional product testing for therapeuticmanufacturing involve analysis tools such as NMR, Mass Spec, Raman, NIRand more. Though some of these tools can be implemented at the output ofthe Bio-MOD (Raman, NIR, and Fluorescence), some methods are not asfeasible (NMR, Mass Spec). Thus, to supplement the gap in producttesting, the present Bio-MOD uses machine learning. Importantly, becauseof the sensor data and the fingerprint profiles that each batchprovides, the results of each run, such as a series of training runs,can be used to populate information relating to product quality andpotency. Such data is burned into a memory chip uniquely for eachbiologic to be made (for example G-CSF). The number of training runs isdetermined as the project progresses and based on statistical Analysisof variance (ANOVA) of all of the profiles. In order to incorporatevarious sensor fingerprints, the Bio-MOD system uses a multi-sensor datafusion approach, capturing statistical significance across themulti-sensor array. The data is pre-processed, and informative featuresare extracted using vector analysis methods.

The artificial intelligence (AI) deep learning module utilizes a trainedclassifier along with security protocol inputs to perform real-timerelease testing of the final sample. Model parameters and classificationresults are connected to a server in the cloud or a ground server,updating the learning model and validating the process with a verifieddatabase. Google has recently announced the availability of a secure,cloud-based AI platform that allows any user access to their new TensorProcessing Units (TPUs). This is a potential game changer for biologicsmanufacturing, since many of the relevant data are specific to temporalevents (such as the spectrum of a peak eluting at a certain time, ranunder a certain buffer composition, flow rate, pressure etc.) and arereadily represented as matrices or tensors. For the Bio-MOD, TPUs isused to perform real-time matrix decomposition and artificial neuralnetwork implementation needed for feature extraction and parameterestimation used in the machine learning module.

In addition, a camera-based video image device observes all the fluidicsand detects any bubbles, leaks, debris etc. that may result fromabnormal operation. Based on just the UV and pressure sensor profiles,the Bio-MOD system has already demonstrated the capability to estimatepurity and concentration, detect process deviations and reject a run.Through the development and testing of the Bio-MOD devices, a range ofdifferent conditions and product quality attributes has been recorded.Extracting group features from this multi-modal sensor data, it has beenshown that the data fits these features closely (R>0.9) to productquality attributes (purity, aggregation, concentration) using artificialneural networks (ANN) and support vector machines (SVM) as shown in FIG.10 .

Neural Networks for Blind-Source Separation

Frequently, machine learning systems are used to process data. Forexample, machine learning systems can be used to perform informationretrieval or rank data items. The term machine learning system isgenerally intended to refer to a computer-related entity, eitherhardware, a combination of hardware and software, software, and/orsoftware in execution.

The training of a learning system can be further explained by looking ata specific example. For example, the learning component can include aneural network. Neural networks are commonly used for classification. Aneural network is commonly organized as a multilayered, hierarchicalarrangement of processing elements, also referred to as neurons, nodesor units. In a hierarchical arrangement of neurons in a neural network,the neurons are usually arranged into layers. The output of a neuron inone layer can be an input to one or more neurons in a successive layer.Layers may be exposed in the sense that either the inputs of neurons inthat layer directly receive input from a data source external to theneural network or the outputs of neurons are the desired result ofprocessing. Layers may also be hidden in the sense that the inputs ofunits in that layer are computed using the outputs of units in aprevious or lower layer, and the outputs of units in a hidden layer feedinputs for units in a successive or higher layer. An exemplary neuralnetwork can include any suitable number of layers such as an inputlayer, an intermediate or hidden layer and an output layer.

Blind source separation (BSS) is the art of separating out the sourcesignals, with as its only assumption that these signals arestatistically independent. In most BSS algorithms the additionalassumption is made that that the mixing is linear. Sensors are sometimesused to observe a mixture of source signals. One known approach to BSSis independent-component analysis (ICA) which is an extension of alinear transform called Principal Component Analysis (PCA). It is aimedat extracting the independent sources when the source signals are activesimultaneously and is a BSS algorithm depending on using the ArtificialNeural Networks. (28-29)

A neural network (NN), in the case of artificial neurons calledartificial neural network (ANN) is an interconnected group of artificialneurons that uses a mathematical or computational model for informationprocessing based on a connectionist approach to computation. In mostcases an ANN is, in formulation and/or operation, an adaptive systemthat changes its structure based on external or internal informationthat flows through the network. Modern neural networks are non-linearstatistical data modeling tools. They are usually used to model complexrelationships between inputs and outputs or to find patterns in data. Inmore practical terms neural networks are non-linear statistical datamodeling or decision making tools. They can be used to model complexrelationships between inputs and outputs or to find patterns in data.

Specifically, FIG. 10 shows the results of eighteen device runs from astudy in mice of Bio-MOD produced G-CSF. These results were analyzedusing ANN. For a quick review, artificial neural networks arecomputational systems, based on biological neural networks. ANNs havebeen used in a wide range of applications where extraction ofinformation or patterns from potentially noisy input data is required.Such applications include character, speech and image recognition,document search, time series analysis, medical image diagnosis and datamining. As discussed above, neural networks typically comprise a largenumber of interconnected nodes. In some classes of neural networks, thenodes are separated into different layers, and the connections betweenthe nodes are characterized by associated weights. Each node has anassociated function causing it to generate an output dependent on thesignals received on each input connection and the weights of thoseconnections. Neural networks are adaptive, in that the connectionweights can be adjusted to change the response of the network to aparticular input or class of inputs. Conventionally, artificial neuralnetworks can be trained by using a training set comprising a set ofinputs layers, layers and output layers. The goal of training is to tunea network's parameters so that it performs well on the training set and,importantly, to generalize to untrained test data.

In this ANN method, data from the GMP production runs of G-SCF-His, asshown in FIGS. 10A and 10B (results); the top four principal componentweights were extracted and input into a 2-layer feed forward ANN with a10 hidden neuron layer. The product purity attributes of thecorresponding runs where binned into three ranges: (output layers p₁,p₂, p₃) greater than 99%, between 98% and 99%, and less than 98%. TheANN was trained with the Levenberg-Marquardt algorithm using 12 runs,validated using 2 runs, and tested using 2 runs. Repeated training ofthis network gave a high correlation between the multi-sensor data (UVabsorbance, Pressure, and Raman spectra) and the product purityestimates (R>0.9) and was able to estimate the correct purity bin with acertainty of at least 90% probability. The learning algorithmdemonstrated the ability of the system to predict product purityestimates which closely matched the experimental results as verified byhigh sensitivity silver strain as shown by the chromatography data shownin Figure FIG. 10D shows the ability to predict the correct purity witha certainty of at least 90% probability.

Further, use of vector decomposition methods on a diverse set of batchruns such as sparse Independent Component Analysis (sparse-ICA) andIndependent Vector Analysis (IVA) have been shown to extractinterpretable features that best represent underlying impurities andsystem faults across the lifetime of the device. The use of theseunderstandable features as inputs into the model addresses the issue oflimited data size and interpretability in the “black box” model formachine learning in the bio-medical field. The addition of all the othersensors as discussed herein makes the Bio-MOD even more robust. Astatistically relevant number of “training” runs are used to define theprocess based on product quality metrics that are determined off-line.The data is rigorously validated with deliberate system perturbations todetermine QbD-driven (quality by design) criteria to define operatingspace where product quality is met and to create product rejectionconditions. Thus, a robust, failsafe, machine learning-driven releasecriterion for each time the system is used to make a biologic. With thisapproach, a real-time electronic batch record is created for every lotmade by every Bio-MOD and used to grow the intelligence of the systemwith integration of multiple runs over time. Accordingly, each run andbatch have a traceable process associated with it for retrospectiveanalysis of any adverse event reported from the biologic

This approach allows the evolution of next-generation, deep learningAI-driven systems to biologics Pharmaceutical Quality/CMC and results insystems that are inherently built for continuous quality monitoring andassurance. This offers unprecedented security and traceability down toevery single run. Clearly the system is evolvable with additionalanalytics introduced on-line into Bio-MOD. With the use of machinelearning the excessive need for post-run analysis (NMR, Mass Spec) isreplaced. The Bio-MOD system of the present invention with the inclusionof machine learning proves that bio-pharma manufacturing can besmall-scale, mobile, and robust. With proliferation of multi-sensor datafrom thousands of small batch runs, machine learning will only becomemore accurate at estimating quality parameters giving manufacturesbetter ability to perform real-time release.

Methods

Lyophilization and stability testing studies of the IVT components. TheIVT system used here has three components: (1) the CHO cell lysate; (2)the reaction mixture; and (3) the dialysis buffer. The CHO cell lysateis an extract from CHO cells and contains the necessary materials fortranscription and translation while allowing for shelf stability, whichis not possible with live cells. The reaction mixture consists of keyingredients needed for the transcription and translation of the targetgene. The dialysis buffer contains reaction supplements that arerequired to support protein expression in a continuous-exchangecell-free (CECF) system by providing a constant supply ofenergy-regenerating substrates to maintain the reaction while removingtoxic byproducts. All three components of the IVT system werelyophilized and tested for stability and consistency in productgeneration using tGFP (turbo green fluorescent protein) as theexpression model. The liquid CHO cell extracts and buffers werelyophilized with 5% sucrose as a lyoprotectant. Lyophilization volumesof 1 ml for the cell extract and 0.875 ml for the dialysis buffer wereput separately in standard 5 ml cylindrical glass vials. For thereaction mixture, 50 ml was lyophilized in a standard 2 ml cylindricalglass vial. Briefly, samples were pre-cooled on frozen shelves kept at−40° C. for 230 min followed by a primary and a secondary drying cycle.The primary freeze drying was carried out at −40° C. for a total of 365min, while gradually raising the temperature to 0° C. Subsequently, asecondary drying cycle was performed for a total of 540 min, whileraising the temperature from 0° C. to 25 C, by which all thetightly-interacting water molecules were removed. At the end of the run,the glass vials were sealed under nitrogen before being removed from thelyophilizer and finally crimped to seal. Stability testing was done inthe specific time points indicated by expressing GFP protein using eachlyophilized component stored in respective conditions in duplicate. GFPwas expressed using duplicate 100 μl reactions for each lyophilizedproduct and quantified by fluorescence relative to a recombinant GFPstandard.

Plasmids. The rDNA encoding the recombinant proteins were sub-clonedinto the IVT expression vector, pT7CFE1-CHis using NdeI and XhoIrestriction sites. The diphtheria toxoid plasmid DT5 was procured fromAddGene (cat. no. 11081). Similarly, rDNA for a truncated version ofhuman GADD34, an accessory protein to the IVT reaction, was sub-clonedinto pT7CFE1-CMyc vector using NdeI and XhoI restriction sites. Plasmidswere transformed into ZYMO DH5α E. coli cells. These cells were allowedto proliferate overnight. The next day, plasmid rDNA was isolated usingthe Zymo-Giga plasmid isolation kit following the manufacturer'sguidelines. GADD34 is co-expressed with the protein of interest.

Preparation of IVT reaction. The 1-Step CHO High-Yield IVT Kit (ThermoFisher Scientific, Rockford, IL) is comprised of lyophilized CHO celllysate and solutions for the reaction mix and dialysis buffer. Allcomponents were allowed to come to room temperature. The lyophilizedelements were reconstituted with nuclease-free water (NFW) and mixedgently. The components were then added in the following order: 1 mllysate, 400 μl 5× reaction mix, 8 μg GADD34 plasmid and 80 μg rDNAplasmid. The mixture was brought to a total volume of 2 ml with NFW.

Preparation of CHO microsomes. The microsomes were isolated from the CHOcells as described. In brief, 2.51 of CHOK1 cell culture (0.6×10 6viable cells ml−1) was used and clarified by centrifugation. Followingcentrifugation at 2,000 g for 5 min at 4° C., the cell pellet wascollected and washed with 100 ml of wash buffer (35 mM Hepes-KOH pH 7.5;140 mM NaCl; 5 mM dextrose). The step was repeated thrice. The cellpellet was then washed with 100 ml of extraction buffer (30 mM Hepes-KOHpH 7.5; 135 mM potassium acetate; 30 mM KCl; 1.65 mM magnesium acetate).Finally, 10 ml of extraction buffer was added to the 10 g cell pelletand lysed using a Dounce homogenizer (four strokes on ice). Thecollected suspension was clarified by centrifugation at 3,000 g for 10min. The supernatant was analyzed on a sucrose gradient and byultracentrifugation. The fractions were collected and stored at −80° C.for further use.

Expression in dialysis cassettes. Reaction components were reconstitutedwith NFW for CECF protein expression format. The IVT reaction wasinjected into a 0.5 to 3 ml size, 10 or 20 kDa MWCO Slide-A-Lyzerdialysis cassette (Thermo Fisher Scientific, Rockford, IL) using asyringe. The excess air is removed by subsequent suction. Loadedcassettes were then immersed in 25 ml of 1× dialysis buffer individuallycontained in a modified dialysis bag. The bag was sealed after theexcess air was removed and placed horizontally in the onboard shakerincubator that was pre-warmed to 30° C. Reactions were carried out for 6h with constant shaking at 30 r.p.m. Alternatively, the reactions wereplaced in a standalone shaker incubator (Certomat BS-1, Sartorius) andcarried out for 6 h at 30° C. with constant shaking at 150 r.p.m.,except for the EPO reaction, which was kept for 8 h at 28° C.

G-CSF-His expression and purification in E. coli. To establish astandard spectrum for NMR spectroscopy, cell-based E. coli-derivedG-CSF-His was prepared. The NMR spectra of the IVT-derived G-CSF-His wascompared with that of the cell-based E. coli-derived G-CSF-His. Thepreparation of the E. coli-derived G-CSF-His is described below. E. coliexpression was carried out using Shuffle express competent E. coli cells(NEB Inc., Ipswich, MA, cat. no. C3028H) using manufacturer's protocolsfor transformation and expression. Minor variations in the expressionprotocol were as indicated here: a single colony was grown overnight in5 ml LB media with ampicillin at 30° C. 1 ml of overnight culture wasused to inoculate three 100 ml expressions containing 2×LB mediacontaining 100 μg ml⁻¹ ampicillin and incubated at 30° C., until an ODof was reached. Each expression was then induced with 50 μl of a 1 MIPTG stock to achieve a final IPTG concentration of 0.5 mM. The growthtemperature was reduced to 16° C. for overnight expression and the E.coli cells harvested the next day by centrifuging at 8,000 g for 15 min.The supernatant was discarded and the cells were re-suspended in 5 ml ofcolumn buffer 1 (1×PBS, 500 mM NaCl, 10 mM imidazole). The re-suspendedcells were placed on ice and lysed by sonication with 5-7 watts, using30-seconds-on and 1-minute-off cycles. Whole lysate from each of thethree expressions was collected (WL1, WL2 and WL3) and centrifuged at12,000 g for 15 min. The clarified lysate (supernatant) was collected(CL1, CL2 and CL3) and combined to a total of 15 ml, then passed througha 10 ml poly-prep gravity flow column (Bio-Rad, Hercules, CA) packedin-house with 1.5 ml HisPur Ni-NTA resin (Thermo Fisher Scientific,Rockford, IL, cat. no. 88221). The column was preequilibrated with 10column volumes (CV) of buffer 1. A 15 ml sample of the firstflow-through (FT1) was obtained and passed through the column a secondtime to collect a second flow-through (FT2). The column was washedtwice: (1) for 10 CV with buffer 1 and; (2) for 2 CV with buffer 2(1×PBS, 500 mM NaCl, 50 mM imidazole). Finally, the column was washedfour times using 2 ml elution buffer (1×PBS, 500 mM NaCl, 250 mMimidazole) to elute the final protein product collected at a finalvolume of 8 ml.

Spin purification of His-tagged protein. All materials and reagents werepurchased from Thermo Scientific, unless otherwise noted. Purificationwas done by immobilized metal affinity chromatography (IMAC) usingHisPur cobalt spin columns (1 ml) packed in-house with HisPur cobaltresin. Volume ratio of resin to sample was kept at 1:5. Samples werediluted 5 times with binding buffer (10 mM imidazole in PBS, pH 7.4)before loading to the column. Buffers were freshly made and filteredusing 0.2 μm filter (Corning, NY, USA). Two wash steps were performed;first using the loading buffer, followed by a second buffer containing30 mM imidazole in PBS. For elution, 150 mM imidazole in PBS buffer wasused. Columns were centrifuged using a Sorvall Legend XTR (ThermoScientific) at 100 g for 1 min at 4° C. after each wash or elution.

Quantitative silver-stained SDS-PAGE. Criterion TGX precast midi proteingel (4-20%) was used in the experiment following standard protocol witha Criterion electrophoresis cell, both obtained from Bio-Rad (cat. no.1656001 and no. 5671093). For staining gels, ProteoSilver plus silverstain kit (Sigma-Aldrich, cat. no. PROTSIL2) was used. Knownconcentrations of cell-derived glucose binding protein (GBP), G-CSF(LifeTechnologies) or bovine serum albumin (BSA) were loaded along eachsample and used as standard reference for determining the purity and theconcentration of purified protein. The area of each band was measuredwith ImageJ software and the concentration calculated relative to thestandard curve. Percent purity was determined also through the sameimage analysis software by taking the ratio of the area of the known,lowest detectable band versus the total area, where the total area isequal to the area of the lowest detectable protein band plus area ofimpurities in an overloaded gel.

660 nm assay. Quantitative protein analysis was done using Pierce 660 nmprotein assay kit (Thermo Scientific, cat. no. 22660) following themanufacturer's recommended protocol.

Capillary electrophoresis (CE-SDS) of G-CSF (LabChip protein assay).Capillary electrophoresis was done in the LabChip GXII instrument, usinga LabChip HT Protein Express 200 assay (PerkinElmer, Hopkinton, MA)following the manufacturer's recommended protocol. Samples weredenatured at 95° C. (instead of 100° C.) for 5 min.

Reversed-phase high performance liquid chromatography (RP-HPLC) ofG-CSF. IMAC-purified samples were analyzed on a Dionex Ultimate 3000series HPLC system (Thermo Fisher Scientific, Bannockburn, IL) using aBioBasic C18 column (Thermo scientific). Mobile phase A consists of 0.1%TFA in water while mobile phase B consists of 0.1% TFA in acetonitrile.Gradient elution of 30-100% B in 25 min at a flow rate of 0.5 ml min⁻¹was used.

Bioactivity assay for G-CSF. The standard method for the in vitrobioassay for G-CSF activity was based on the measurement of cellproliferation utilizing the murine myeloid leukemia cell line NFS-60(ATCC CRL-1838). The proliferation of NFS-60 cells in response tovarying concentrations of standard G-CSF and IVT-produced samples wasquantified using the MTT cell proliferation assay kit (ATCC 30-1010 K).Reference standard was purchased from Life Technologies (cat. no.PHC2033) or WHO (NIBSC cat. no. 09/136). Activity results presented herewere determined from freshly-produced IVT samples, characterized within24 h after production. Briefly, yellow 3-(4, 5-dimethylthiazolyl-2)-2,5-diphenyltetrazolium bromide (MTT) is reduced by dehydrogenase enzymesin metabolically active cells to purple-colored formazan. Addition ofSDS detergent disrupts the cells releasing the formazan, which is thenquantified by spectrophotometric means. Controls include an IVT blank(without DNA) and a sample boiled at 100° C. for 10 min to destroy G-CSFactivity.

Western blot. The G-CSF samples were diluted 10× with PBS. 20 μlaliquots were treated with 6 μl of 5× diluted Laemmli buffer dye, boiledat 100° C. for 5 min, then loaded to a pre-cast 12.5% tris-HCl gel(Bio-rad, cat. no. 3450014) and run at 100 V. Samples were thentransferred to a nitrocellulose membrane (Bio-rad, cat. no. 1620233) andleft in 20 ml blocking buffer overnight. Primary antibody (Rabbitanti-G-CSF, Abcam, cat. no. 9691) was added at a concentration of1:3,000 to 20 ml blocking buffer the next day, removed after an hour,and the blot was washed with PBST. Fresh blocking buffer (20 ml) wasthen added with a complementary HRP-conjugated secondary antibody (GoatAnti-Rabbit HRP, Abcam, cat. no. ab6721) at a concentration of 1:3,000.Solution was removed after 1 h, and the blot was washed with PBST.Finally, a chemiluminescent substrate (Thermo Scientific, cat. no.34075) was added to the blot and imaged using a Thermo Scientific myECLImager. The procedure for the western blot of GBP was similar to that ofG-CSF except the anti-His antibody was used for identification.

For the EPO western blot analysis, samples were first treated withPNGase. The PNGase and its components were purchased from New EnglandBiolabs, MA, USA. Briefly, an 18 μl sample was added to a 1.5 mlEppendorf tube along with 2 μl of 10× denaturing buffer. The sampleswere vortexed for 3-5 s and then boiled at 100° C. in a heater (Thermomixer comfort, USA) for 10 min to enable optimal denaturation. After the10 min incubation at room temperature, 4 μl of 10×G7 buffer and 4 μl of10% NP-40 were added to the tube. For reactions with PNGase F, 8 μl ofnuclease-free water and 4 μl of PNGase F were added to the reaction. Forthe reactions without PNGase F, 12 μl of nuclease free water was addedand the reaction was incubated at 37° C. for 6 h. Aliquots (20111) weretreated with 6 μl of 5× diluted Laemmli buffer dye, boiled at 100° C.for 5 min, then loaded to a precast 12.5% Tris-HCl gel (Bio-rad, cat.no. 3450014) and run at 120 V. Samples were then transferred toPolyvinylidene difluoride (Bio-Rad, cat. no. 162-1075) and left in 20 mlblocking buffer for 1 h. Primary antibody (Rabbit anti-EPO, Abcam, cat.no. ab126876) was added at a concentration of 1:3,000 to 20 ml blockingbuffer after 1 h in blocking solution. The blot is washed with PB ST.Fresh blocking buffer (20 ml) was then added with a complementaryHRP-conjugated secondary antibody (Goat Anti-Rabbit HRP, Thermo, cat.no. 31460) at a concentration of 1:3000. Solution was removed after 1 h,and the blot was washed with PBST. Finally, a chemiluminescent substrate(Thermo Scientific, cat. no. 34076) was added to the blot and imagedusing a Thermo Scientific myECL Imager.

Quantitative analysis of G-CSF by ELISA. The concentration of G-CSF inthe cell extracts was determined using a quantitative Sandwich ELISA(G-CSF Human ELISA, Abcam, USA) following the manufacturer'sinstructions. All materials required for the analysis was provided inthe kit. For the standard, a dilution series containing 0 to 500 pg ml⁻¹of G-CSF standard was prepared. The clarified samples were dilutedaccordingly with a buffer containing % BSA in PBS (pH 7.2) and analysedin triplicate. Briefly, 100 μl of each standard and sample was added toappropriate wells and incubated for 2.5 h at room temperature. The wellswere washed with wash buffer and added with 100 μl of biotinylated G-CSFantibody and further incubated for 1 h at room temperature. Afterwashing with wash buffer, 100 μl of HRP-streptavidin solution was addedto each well and incubated for 45 min at room temperature. Then, 100 μlof TMB substrate solution was added followed by incubation for 30 min,and the reaction subsequently halted by adding 50 μl of stop solution.Finally, absorbance at 450 nm was measured using a SpectraMax M5Multi-mode microplate reader (Molecular Devices, Sunnyvale, CA).

The amount of EPO in the reaction mixtures were determined using theEPO-specific quantitative ELISA kit (Quantikine IVD ELISA, R&D Systems,Minneapolis, MN, USA) following the manufacturer's instructions. Thesupernatant was diluted with 0.1% (w/v) BSA in phosphate bufferedsaline. Standard recombinant EPO ranging from 0-200 mIU ml⁻¹ wasprocessed in parallel according to the manufacturer's instructions.Briefly, 100 μl of EPO assay diluent was added to each well followed by100 μl each standard and sample was added to appropriate wells andincubated for 2 h at room temperature. The wells were aspirated andadded 200 μl of conjugate and further incubated for 2 h at roomtemperature. After washing with wash buffer, 200 μl of substratesolution was added to each well and incubated for 25 min at roomtemperature, and the reaction subsequently stopped by adding 100 μl ofstop solution. Finally, absorbance at 450 nm was measured using aSpectraMax M5 Multi-mode microplate reader (Molecular Devices,Sunnyvale, CA).

NMR spectroscopy. Sample preparation. Each NMR spectrum was collected onan 850-MHz NMR spectrometer using 140-300 μl G-CSF-His samples in a 4 mmShigemi tube (cat. no. BMS-004J, Shigemi Inc., PA) at 27° C. NMR sampleswere prepared as follows: Immediately after elution from the HisPurcolumn, G-CSF-His fractions were pooled and diluted with NMR buffer(50-70 mM sodium phosphate at a pH of 3.5±0.1). This was concentrated to0.3 ml using to a pre-washed Amicon Ultra-15 centrifugal filtration unit(cat. no. UFC901024, EMD Millipore, MA). Solution was further dilutedten-fold with the NMR buffer and re-concentrated; this buffer exchangeprocess was repeated four more times. For the 140 μl sample size, thesample was further concentrated using the Amicon Ultra-0.5 centrifugalfilters (cat. no. UFC501024). Final NMR samples contained 95% H₂O/5%D₂O.

Solution NMR spectroscopy. 1D proton NMR spectra were collected usingthe standard Presat pulse program available in the Brüker library.Although protein preparation could be scaled up to produce millimolarsamples for 2D NMR studies, yields from the IVT process were anticipatedto be significantly smaller. To meet this sample limitation, design ofthe NMR samples and experiments required optimization. Tests ofunlabeled lysozyme and ubiquitin standards at 1 mM concentration usingthe 1H,15N-SOFAST-HMQC pulse program indicated an 8- to 9-fold reductionin acquisition time compared to a standard 1H,15N-HSQC (data not shown).It suggested that the G-CSF-His sample concentration could be lowered toabout 0.1 mM if the acquisition time were left at about 20 h. Thisstrategy enabled the successful 1H,15N-SOFAST-HMQC data collection forIVT-produced G-CSF-His at natural abundance of the 15N isotope.

Amino acid sequence analysis. Sample preparation. The lyophilized sample(50-100 μg) was dissolved in 300 μl of 8 M guanidine hydrochloride (pH8, adjusted with triethylamine), reduced with dithiothreitol (DTT) for 1h, and alkylated with iodoacetamide (IAA) for 1 h. Solution was thentransferred to a 3 kDa MWCO membrane and dialyzed against 5 l of Milli-Qwater for 3 h. Water was replaced and the sample was dialyzed again for16 h. Dialyzed sample was then transferred to a 2 ml tube and evaporatedto dryness in a vacuum centrifuge. After drying, the sample wasre-suspended in 200 μl of 50 mM ammonium bicarbonate buffer (pH=8) anddigested with chymotrypsin (1:50 enzyme-to-substrate ratio) at 37° C.with shaking for 16 h. The reaction was quenched with the addition of200 μl of 0.1% trifluoroacetic acid (TFA) solution.

Liquid chromatography mass spectrometry (LC-MS) analysis. Analysis wasperformed on a Thermo Scientific Orbitrap Fusion Tribrid massspectrometer equipped with an EASY-Spray source and Dionex UltiMate 3000RSLCnano System using a 50 cm C18 column (EASY-Spray column: 50 cm×75 μmID, PepMap RSLC C18, 2 μm). 2-4 μl of sample corresponding to ˜500 ng ofdigest material was subjected to analysis over an 80 min linear LCgradient (Start: 97% A, 3% B; End: 55% A, 45% B; A=0.1% formic acid inwater; B=0.1% formic acid in acetonitrile). Data was processed usingThermo Scientific Proteome Discoverer 1.2 software. RAW files weresearched using Sequest HT search engine against a database containinghuman, yeast, bovine, E. coli, as well as the sequences of the expressedproteins.

Bio-MOD operation. The Bio-MOD is operated using a program written inLabView. The program includes auto-priming of the pre-assembledbioprocess fluid train with a limited interactive checklist ofoperations. Following completion of the auto-priming, the Bio-MODprocess is fully automated with self-monitoring capabilities, producingthe purified sample at the end. The protein is expressed for 6 h in theshaker/incubator (Certomat BS-1, Sartorius) at 30° C. and 150 r.p.m.Briefly, the syringe pumps and their contents are as follows (eachbuffer is at pH=7.4):

-   -   Pump I: lysate from IVT reaction    -   Pump II: loading/binding/wash buffer 1=1×PBS    -   Pump III: wash buffer 2=1×PBS+40 mM imidazole+300 mM NaCl    -   (For EPO: 1×PBS+0.5% Tween 20+40 mM imidazole+300 mM NaCl)    -   Pump IV: elution buffer=1×PBS+250 mM imidazole    -   (For EPO: 1×PBS+0.5% Tween 20+500 mM imidazole)    -   Pump V: polishing buffer=20 mM phosphate buffer+50 mM arginine

The fluid train is pre-assembled using non-DEHP Tygon tubing ( 1/16″ID×⅛″ OD) and peroxide-cured silicone tubing with barbed luer-lockconnections, one-way check-valves, and a microfluidic snake mixerdeveloped in-house. Smaller diameter PTFE tubing ( 1/32″ ID× 1/16″ OD)is used between the purification column and UV sensors to reduce peakbroadening, and barbed 2 psi check valves are connected to allbioprocess outlets to keep the bioprocess pressurized. Disposable BDsyringes are filled with the corresponding buffers and mounted onto thesyringe pumps. Silicone tubing is used in specific sections where pinchvalves are present in the fluidics.

The priming of the fluid train is performed automatically with 3 to 4interactive dialogue boxes for direct user interaction and to helpmitigate issues such as air bubbles and leaks. The final step of theauto-priming includes the insertion of a 1 ml HisPur Cobalt affinitycolumn and a 5 ml HiTrap DEAE Fast Flow polishing column in the system.After insertion, the program pre-saturates the columns with 10 columnvolumes (CVs) of binding and polishing buffers respectively. After theauto-priming, the UV sensor readings are checked to fall within theacceptable range of 260-300 mV. The program then computes a baselineaverage for the UV absorbance, and a labelled product vial is placed inthe polished sample compartment for automatic collection. Thepurification script is loaded onto the computer and the automationsettings for the purification are reviewed one last time. The bioreactoris removed from the incubator at the end of the 6 h reaction and placedin the Bio-MOD cassette holder. The system is then ready for automatedpurification and sample collection. The collected product at the end ofthe process is immediately stored at 4° C. and subsequentlycharacterized offline.

Materials testing for leachables and extractables. The extractable andleachable studies were carried out as per the guidelines in the‘Regulatory Compliance Standardized Extractable Protocol for Single-UseSystems’ by the BioPhorum Operations Group (BPOG) to screen thematerials used in the Bio-MOD processes. The preliminary E&L tests usedsix solvents 50% ethanol, 0.5 N NaOH, 0.1 M phosphoric acid, 1% PS-80, 5M NaCl and WFI (water for injection) that were tested on the materialsused in the system. The samples were immersed in the solvents with aSA/V of 6:1 at 40° C. for 24 h. The extractables were extracted into theorganic dichloromethane and analysed for semi-volatiles using directinjection GC-MS. The peaks were compared with the 1 ppm phenanthrened-10 as the internal standard. The results indicated that the materialsdid not have any extractables exceeding the 1 ppm phenanthrene d-10internal standard. The Slide-A-Lyzer dialysis cassette (bioreactor) wastested for volatiles using Headspace GC-MS and 1 ppm toluene as theinternal standard. The results indicated except for the 50% ethanolsolvent, there were no volatiles from the dialysis cassette.

Two-dimensional chromatography. For the two-dimensional chromatographymethod, the first dimension employed a ProPac SAX-10 strong-baseanion-exchange column with buffer A consisting of 10 mM Tris and 5%acetonitrile at pH 8.5 and buffer B consisting of 10 mM Tris, 0.5 M NaCland 5% acetonitrile also at pH 8.5. 80 μl of CHO lysate was used as thefeed sample and a flow rate of 0.046 ml min⁻¹ was used. After sampleinjection, the column was washed with 2.4 ml buffer A and then agradient from 0% buffer B to 50% Buffer B in 322 min was employedfollowed by a gradient from 50% buffer B to 100% buffer B in 111 min.Every 22 min one fraction of about 1 ml was collected and directed tothe second chromatographic dimension. For the second chromatographicdimension an Accucore-150-C4 reversed-phase column was used. Buffer Awas composed of 5% acetonitrile in water with 0.1% trifluoroacetic acid(TFA). Buffer B was 0.1% TFA in acetonitrile. A flow rate of 0.4 mlmin⁻¹ was used and after each injection the column was first washed with26% buffer B for 1 min, then a gradient from 26% buffer B to 50% bufferB in 8 min was employed, followed by a gradient from 50% B to 95% bufferB in 2 min. After holding at 95% buffer B for 3 min the column wasre-equilibrated with 26% buffer B for 8 min and then another sample wasinjected. To maintain sample stability the whole process was conductedat 5° C.

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1.-17. (canceled)
 18. A method of analyzing the purity and quality of aprotein produced in a portable, cell-free bioprocessing system, whereinthe bioprocessing system comprises a protein expression module, aprotein purification module, and an artificial intelligence (AI) machinelearning module, the method comprising: obtaining at least pressure andUV sensor data from the protein purification module during a productionprocess for the produced protein; transmitting the data to a computeraided classification system; extracting features from the data with thecomputer aided classification system for classifying the producedprotein and process conditions, wherein extracted features characterizethe produced protein and such sample characterization is compared tocharacterization of previously extracted features to provide classifiedfeatures of the produced protein; and applying an unsupervisedclustering process to the classified features to provide a plurality ofoutput clusters to provide enhanced identification of the producedprotein during the production process.
 19. The method of claim 18,wherein the protein expression module and the protein purificationmodules are associated with on-board analytics,
 20. The method of claim19, wherein the on-board analytics comprise multiple sensors forcollecting data during the production of the on-demand synthesizedprotein, to be analyzed by the AI machine learning module, and whereinthe AI machine learning module collects and stores in-line real-timetesting data of purified protein from the protein purification moduleand provides information on product quality and potency for each batchof the on-demand synthesized protein relative to previously producedproteins.
 21. The method of claim 18, wherein the protein expressionmodule comprises at least one dialysis cassette or reactor for inclusionof cell lysate, a reaction mixture, and DNA or mRNA for production ofthe on-demand synthesized protein.
 22. The method of claim 18, whereinthe purification module comprises two UV sensors.
 23. The method ofclaim 22, wherein the purification module further comprises amultiplicity of programmable syringe pumps and pressure sensors thatgenerate in-line real-time testing data during a two-step purificationprocess.
 24. The method of claim 21, wherein the cell lysate is from CHOcells or E. coli cells.
 25. The method of claim 21, wherein the reactionmixture comprises at least one of amino acids, nucleotides, co-factors,enzymes, ribosomes, tRNA, polymerases, and transcriptional factors. 26.The method of claim 18, wherein the protein purification modulecomprises a metal ion affinity chromatography column for initialpurification and an ion-exchange chromatography column for a polishingstep.
 27. The method of claim 23, wherein each purification processcomprises an inline UV sensor comprising an in-line flow cell and lightsources and detectors for measuring UV absorbance at about 280 nm tomonitor the two-step purification process.
 28. The method of claim 19,wherein the on-board analytics comprise multiple sensors for collectingdata during the production process to be analyzed by a cloud-basedmachine learning system.
 29. The method of claim 28, wherein themultiple sensors measure for dissolved oxygen, pH, absorbance, pressureand temperature.
 30. The method of claim 18, wherein the AI machinelearning module uses a blind source separation (BSS) algorithm.
 31. Themethod of claim 30, wherein the (BSS) algorithm usesindependent-component analysis (ICA) that extracts independent sourcesignals when the source signals are active simultaneously and is a BSSalgorithm depending on using Artificial Neural Networks.
 32. The methodof claim 23, wherein each of the two purification processes comprise atleast one real-time test selected from the group consisting ofabsorbance, circular dichroism, fluorescence measurements, and lifetimemeasurements.
 33. The method of claim 18, wherein the AI machinelearning module is a cloud-based server or a physical server connectedto the bioprocessing system.
 34. The method of claim 18, wherein thedata is captured by a smart phone app and transferred through asmartphone to a server for analysis.
 35. The method of claim 18, whereinthe data is evaluated and an output is provided to the bioprocessingsystem, wherein the output comprises visual data selected from bargraphs, frequency graphs, and/or audio signals.
 36. The method of claim18, wherein the bioprocessing system further comprises a microfluidicmixer positioned between the protein expression module and the proteinpurification module to mix expressed protein with buffer.
 37. The methodof claim 18, wherein UV and pressure sensor profiles are used toestimate purity and concentration of the produced protein, detectproduction deviations, and reject a batch of the produced protein.